Circulating tumoral DNA (ctDNA), commonly named “liquid biopsy”, has emerged as a new promising noninvasive tool to detect biomarker in several cancers including lung cancer. Applications involving molecular analysis of ctDNA in lung cancer have increased and encompass diagnosis, response to treatment, acquired resistance and prognosis prediction, while bypassing the problem of tumor heterogeneity. ctDNA may then help perform dynamic genetic surveillance in the era of precision medicine through indirect tumoral genomic information determination. The aims of this review were to examine the recent technical developments that allowed the detection of genetic alterations of ctDNA in lung cancer. Furthermore, we explored clinical applications in patients with lung cancer including treatment efficiency monitoring, acquired therapy resistance mechanisms and prognosis value.

RAS proteins (KRAS, NRAS and HRAS) are frequently activated in different cancer types (e.g., non-small cell lung cancer, colorectal cancer, melanoma and bladder cancer). For many years, their activities were considered redundant due to their high degree of sequence homology (80% identity) and their shared upstream and downstream protein partners. However, the high conservation of the Hyper-Variable-Region across mammalian species, the preferential activation of different RAS proteins in specific tumor types and the specific post-translational modifications and plasma membrane-localization of each paralog suggest they could ensure discrete functions. To gain insights into RAS proteins specificities, we explored their proximal protein–protein interaction landscapes using the proximity-dependent biotin identification technology (BioID) in Flp-In T-REx 293 cell lines stably transfected and inducibly expressing wild type KRAS4B, NRAS or HRAS. We identified more than 800 high-confidence proximal interactors, allowing us to propose an unprecedented comparative analysis of wild type RAS paralogs protein networks. These data bring novel information on poorly characterized RAS functions, e.g., its putative involvement in metabolic pathways, and on shared as well as paralog-specific protein networks that could partially explain the complexity of RAS functions. These networks of protein interactions open numerous avenues to better understand RAS paralogs biological activities.